research-article

Mining anatomical, physiological and pathological information from medical images

Authors:

Xiang Sean Zhou,

Vikas C. Raykar,

Gerardo Hermosillo,

Zhipang PengAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 14, Issue 1

Pages 25 - 34

https://doi.org/10.1145/2408736.2408741

Published: 10 December 2012 Publication History

Abstract

The field of medical imaging has shown substantial growth over the last decade. Even more dramatic increase was observed in the use of machine learning and data mining techniques within this field. In this paper, we discuss three aspects related to information mining in the domain of medical imaging: the target user groups (for whom), the information to mine (what), and technologies to enable mining (how). Specifically, we focus on three types of information: anatomical, physiological and pathological, and present use cases for each one of them. Furthermore, we introduce representative methods and algorithms that are effective for solving these problems. We conclude the paper by discussing some major trends in the related domains for the coming decade.

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Cited By

Freedman DBagga BMelamud KO’Donnell TVega EWesterhoff MDane B(2024)Quality assessment of expedited AI generated reformatted images for ED acquired CT abdomen and pelvis imagingAbdominal Radiology10.1007/s00261-024-04578-0Online publication date: 18-Sep-2024
https://doi.org/10.1007/s00261-024-04578-0
Ichikawa SItadani HSugimori H(2022)Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithmPhysical and Engineering Sciences in Medicine10.1007/s13246-022-01153-z45:3(835-845)Online publication date: 6-Jul-2022
https://doi.org/10.1007/s13246-022-01153-z
Zhang GLiang ZLai HLin YLin DLi Z(2016)A Novel Deep Model for Biopsy Image Grading2016 International Conference on Information System and Artificial Intelligence (ISAI)10.1109/ISAI.2016.0075(323-326)Online publication date: Jun-2016
https://doi.org/10.1109/ISAI.2016.0075

Index Terms

Mining anatomical, physiological and pathological information from medical images
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 14, Issue 1

June 2012

55 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/2408736

Issue’s Table of Contents

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 December 2012

Published in SIGKDD Volume 14, Issue 1

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Cited By

Freedman DBagga BMelamud KO’Donnell TVega EWesterhoff MDane B(2024)Quality assessment of expedited AI generated reformatted images for ED acquired CT abdomen and pelvis imagingAbdominal Radiology10.1007/s00261-024-04578-0Online publication date: 18-Sep-2024
https://doi.org/10.1007/s00261-024-04578-0
Ichikawa SItadani HSugimori H(2022)Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithmPhysical and Engineering Sciences in Medicine10.1007/s13246-022-01153-z45:3(835-845)Online publication date: 6-Jul-2022
https://doi.org/10.1007/s13246-022-01153-z
Zhang GLiang ZLai HLin YLin DLi Z(2016)A Novel Deep Model for Biopsy Image Grading2016 International Conference on Information System and Artificial Intelligence (ISAI)10.1109/ISAI.2016.0075(323-326)Online publication date: Jun-2016
https://doi.org/10.1109/ISAI.2016.0075
Zhang PCao WObradovic Z(2013)Learning by aggregating experts and filtering novices: a solution to crowdsourcing problems in bioinformaticsBMC Bioinformatics10.1186/1471-2105-14-S12-S514:S12Online publication date: 24-Sep-2013
https://doi.org/10.1186/1471-2105-14-S12-S5

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